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Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods. REMOTE SENSING 2022. [DOI: 10.3390/rs14061414] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Glacier surface facies are valuable indicators of changes experienced by a glacial system. The interplay of accumulation and ablation facies, followed by intermixing with dust and debris, as well as the local climate, all induce observable and mappable changes on the supraglacial terrain. In the absence or lag of continuous field monitoring, remote sensing observations become vital for maintaining a constant supply of measurable data. However, remote satellite observations suffer from atmospheric effects, resolution disparity, and use of a multitude of mapping methods. Efficient image-processing routines are, hence, necessary to prepare and test the derivable data for mapping applications. The existing literature provides an application-centric view for selection of image processing schemes. This can create confusion, as it is not clear which method of atmospheric correction would be ideal for retrieving facies spectral reflectance, nor are the effects of pansharpening examined on facies. Moreover, with a variety of supervised classifiers and target detection methods now available, it is prudent to test the impact of variations in processing schemes on the resultant thematic classifications. In this context, the current study set its experimental goals. Using very-high-resolution (VHR) WorldView-2 data, we aimed to test the effects of three common atmospheric correction methods, viz. Dark Object Subtraction (DOS), Quick Atmospheric Correction (QUAC), and Fast Line-of-Sight Atmospheric Analysis of Hypercubes (FLAASH); and two pansharpening methods, viz. Gram–Schmidt (GS) and Hyperspherical Color Sharpening (HCS), on thematic classification of facies using 12 supervised classifiers. The conventional classifiers included: Mahalanobis Distance (MHD), Maximum Likelihood (MXL), Minimum Distance to Mean (MD), Spectral Angle Mapper (SAM), and Winner Takes All (WTA). The advanced/target detection classifiers consisted of: Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), Matched Filtering (MF), Mixture-Tuned Matched Filtering (MTMF), Mixture-Tuned Target-Constrained Interference-Minimized Filter (MTTCIMF), Orthogonal Space Projection (OSP), and Target-Constrained Interference-Minimized Filter (TCIMF). This experiment was performed on glaciers at two test sites, Ny-Ålesund, Svalbard, Norway; and Chandra–Bhaga basin, Himalaya, India. The overall performance suggested that the FLAASH correction delivered realistic reflectance spectra, while DOS delivered the least realistic. Spectra derived from HCS sharpened subsets seemed to match the average reflectance trends, whereas GS reduced the overall reflectance. WTA classification of the DOS subsets achieved the highest overall accuracy (0.81). MTTCIMF classification of the FLAASH subsets yielded the lowest overall accuracy of 0.01. However, FLAASH consistently provided better performance (less variable and generally accurate) than DOS and QUAC, making it the more reliable and hence recommended algorithm. While HCS-pansharpened classification achieved a lower error rate (0.71) in comparison to GS pansharpening (0.76), neither significantly improved accuracy nor efficiency. The Ny-Ålesund glacier facies were best classified using MXL (error rate = 0.49) and WTA classifiers (error rate = 0.53), whereas the Himalayan glacier facies were best classified using MD (error rate = 0.61) and WTA (error rate = 0.45). The final comparative analysis of classifiers based on the total error rate across all atmospheric corrections and pansharpening methods yielded the following reliability order: MXL > WTA > MHD > ACE > MD > CEM = MF > SAM > MTMF = TCIMF > OSP > MTTCIMF. The findings of the current study suggested that for VHR visible near-infrared (VNIR) mapping of facies, FLAASH was the best atmospheric correction, while MXL may deliver reliable thematic classification. Moreover, an extensive account of the varying exertions of each processing scheme is discussed, and could be transferable when compared against other VHR VNIR mapping methods.
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A fuzzy logic decision support model for climate-driven biomass loss risk in western Oregon and Washington. PLoS One 2019; 14:e0222051. [PMID: 31652268 PMCID: PMC6814215 DOI: 10.1371/journal.pone.0222051] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 08/20/2019] [Indexed: 11/19/2022] Open
Abstract
Dynamic global vegetation model (DGVM) projections are often put forth to aid resource managers in climate change-related decision making. However, interpreting model results and understanding their uncertainty can be difficult. Sources of uncertainty include embedded assumptions about atmospheric CO2 levels, uncertain climate projections driving DGVMs, and DGVM algorithm selection. For western Oregon and Washington, we implemented an Environmental Evaluation Modeling System (EEMS) decision support model using MC2 DGVM results to characterize biomass loss risk. MC2 results were driven by climate projections from 20 General Circulation Models (GCMs) and Earth System Models (ESMs), under Representative Concentration Pathways (RCPs) 4.5 and 8.5, with and without assumed fire suppression, for three different time periods. We produced maps of mean, minimum, and maximum biomass loss risk and uncertainty for each RCP / +/- fire suppression / time period. We characterized the uncertainty due to RCP, fire suppression, and climate projection choice. Finally, we evaluated whether fire or climate maladaptation mortality was the dominant driver of risk for each model run. The risk of biomass loss generally increases in current high biomass areas within the study region through time. The pattern of increased risk is generally south to north and upslope into the Coast and Cascade mountain ranges and along the coast. Uncertainty from climate future choice is greater than that attributable to RCP or +/- fire suppression. Fire dominates as the driving factor for biomass loss risk in more model runs than mortality. This method of interpreting DGVM results and the associated uncertainty provides managers with data in a form directly applicable to their concerns and should prove helpful in adaptive management planning.
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Luo J, Wang Y, Jiang D. Rule-based hidden relation recognition for large scale knowledge graphs. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2018. [DOI: 10.3390/ijgi7070274] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.
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A Novel Classification Technique of Landsat-8 OLI Image-Based Data Visualization: The Application of Andrews’ Plots and Fuzzy Evidential Reasoning. REMOTE SENSING 2017. [DOI: 10.3390/rs9050427] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Zhang J, Deng Y. A method to determine basic probability assignment in the open world and its application in data fusion and classification. APPL INTELL 2016. [DOI: 10.1007/s10489-016-0877-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Adamo M, Tarantino C, Tomaselli V, Veronico G, Nagendra H, Blonda P. Habitat mapping of coastal wetlands using expert knowledge and Earth observation data. J Appl Ecol 2016. [DOI: 10.1111/1365-2664.12695] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Affiliation(s)
- Maria Adamo
- Institute of Intelligent Systems for Automation (ISSIA); National Research Council (CNR); Via Amendola 122/D-O 70126 Bari Italy
| | - Cristina Tarantino
- Institute of Intelligent Systems for Automation (ISSIA); National Research Council (CNR); Via Amendola 122/D-O 70126 Bari Italy
| | - Valeria Tomaselli
- Institute of Biosciences and BioResources (IBBR); National Research Council (CNR-IBBR); Via Amendola 165/A 70126 Bari Italy
| | - Giuseppe Veronico
- Institute of Biosciences and BioResources (IBBR); National Research Council (CNR-IBBR); Via Amendola 165/A 70126 Bari Italy
| | - Harini Nagendra
- School of Development; Azim Premji University; PES Institute of Technology Campus, Pixel Park, B Block, Hosur Road Bangalore 560100 India
| | - Palma Blonda
- Institute of Intelligent Systems for Automation (ISSIA); National Research Council (CNR); Via Amendola 122/D-O 70126 Bari Italy
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Hortal J, de Bello F, Diniz-Filho JAF, Lewinsohn TM, Lobo JM, Ladle RJ. Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity. ANNUAL REVIEW OF ECOLOGY EVOLUTION AND SYSTEMATICS 2015. [DOI: 10.1146/annurev-ecolsys-112414-054400] [Citation(s) in RCA: 544] [Impact Index Per Article: 60.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Joaquín Hortal
- Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales (MNCN-CSIC), 28006 Madrid, Spain; ,
- Department of Ecology, Instituto de Ciências Biologicas, Universidade Federal de Goiás, 74001-970 Goiânia, Brazil;
- Center for Ecology, Evolution, and Environmental Changes, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
| | - Francesco de Bello
- Institute of Botany, Czech Academy of Sciences, CZ-379 82 Třeboň, Czech Republic;
- Department of Botany, Faculty of Sciences, University of South Bohemia, 370 05 Budějovice, Czech Republic
| | | | - Thomas M. Lewinsohn
- Departamento de Biologia Animal, Instituto de Biologia, Universidade Estadual de Campinas, 13083-862 Campinas, Brazil;
| | - Jorge M. Lobo
- Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales (MNCN-CSIC), 28006 Madrid, Spain; ,
| | - Richard J. Ladle
- Instituto de Ciências Biológicas e da Saúde (ICBS), Universidade Federal de Alagoas, 57072-900 Maceió, Brazil
- School of Geography and the Environment, University of Oxford, OX1 3QY Oxford, United Kingdom;
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